A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

• URL: http://arxiv.org/abs/2404.00579v2
• Date: Thu, 4 Jul 2024 15:06:42 GMT
• Title: A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
• Authors: Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano,
• Abstract summary: This survey connects key advancements in recommender systems using Generative Models (Gen-RecSys) It covers: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges.
• Score: 57.30228361181045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional recommender systems (RS) typically use user-item rating histories as their main data source. However, deep generative models now have the capability to model and sample from complex data distributions, including user-item interactions, text, images, and videos, enabling novel recommendation tasks. This comprehensive, multidisciplinary survey connects key advancements in RS using Generative Models (Gen-RecSys), covering: interaction-driven generative models; the use of large language models (LLM) and textual data for natural language recommendation; and the integration of multimodal models for generating and processing images/videos in RS. Our work highlights necessary paradigms for evaluating the impact and harm of Gen-RecSys and identifies open challenges. This survey accompanies a tutorial presented at ACM KDD'24, with supporting materials provided at: https://encr.pw/vDhLq.

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